Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become pivotal issue in semantic communications. This paper proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data oriented semantic communications (JTD-SC) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.

翻译：语义通信旨在通过利用源数据的语义特征，以较少的频谱资源完成各类语义任务。为了同时支持数据传输与语义任务，联合数据压缩与语义分析已成为语义通信中的关键问题。本文提出了一种面向任务与数据的联合语义通信（JTD-SC）的深度分离信源信道编码（DSSCC）框架，并利用变分自编码器方法解决含语义失真的率失真问题。首先，通过分析DSSCC框架的贝叶斯模型，基于贝叶斯推断方法推导出一种适用于通用数据分布与语义任务的新型率失真优化问题。其次，针对联合图像传输与分类的典型应用，将变分自编码器方法与前向自适应方案相结合，有效提取图像特征并自适应学习所得特征的密度信息。最后，提出一种迭代训练算法以应对深度学习模型的过拟合问题。仿真结果表明，与经典压缩方案及新兴的深度联合信源信道方案相比，所提方案在多数场景下实现了更优的编码增益以及数据恢复与分类性能。